X-ray analysis techniques have been some of the most significant developments in twentieth-century science and technology. The use of x-ray fluorescence, diffraction, spectroscopy, imaging, and other x-ray analysis techniques has led to a profound increase in knowledge in virtually all scientific fields.
Recent x-ray systems, occasioned in some instances by government regulations (i.e., monitoring the level of sulfur in fuel pipelines) or advanced production requirements (i.e., monitoring the texture of superconducting tapes under production) are confronted with the problems of samples moving past the detectors (referred to herein as dynamically changing samples) creating streams of unpredictably variant measurement data. This type of data stands in contrast to the relatively static data conventionally obtained by laboratory, bench-top x-ray analysis systems.
The expected measurement-to-measurement variance of data itself may change depending on the amplitude range of the measurements. Any measurement system should consider this when determining whether measurement changes are significant (warranting their reporting to the user as output values) or insignificant (warranting de-emphasis or deletion entirely).
What is required, therefore, are techniques, methods and systems which exploit some of the a-priori knowledge of x-ray measurement data, and effectively filter significant changes in measurements from insignificant changes.